1. Field of the Invention
The present invention relates a technology for supporting analysis of gene interaction network.
2. Description of the Related Art
In recent years, research for molecular mechanisms related to diseases is actively performed, based on differences in gene expression states between a patient and a healthy subject. Furthermore, research for effects of drugs on a living body at a molecular mechanism level is also often performed, based on differences in the gene expression states between when the drug is administered and when no drug is administered. In such researches, an assessment of relativity between a gene of interest and a disease or an estimation of effects of drugs on the gene of interest is widely performed through an analysis of gene interactions on individual genes in a gene cluster that is focused based on some clues.
In such analyses, it is preferable to appropriately select genes and the gene interactions to be analyzed depending on relationships with the disease. In other words, in general, a large number of genes of interest are acquired. The number of relevant gene interactions is enormous, and various interactions due different biological factors are present. Therefore, it is preferable to facilitate selection of an analysis subject (subject to be analyzed preferentially) by classification of the interactions according to the molecular mechanisms or by automatic selection of an interaction having high possibility to be related to the disease.
FIG. 21 is a schematic of gene interactions related to a gene cluster of interest, expressed in a network graph. A gene interaction network 2100 is formed by superimposing interaction networks that are respectively formed for a particular gene in each of the approximately 300 gene clusters of interest acquired by a gene expression analysis. A rectangular node indicates a gene. An edge indicates that a known interaction (interaction that has been reported in a document) is present between two genes corresponding to nodes on each end of the edge. As shown, a network that is a collection of interactions related to the genes of interest is often large and has a complex structure. Therefore, a technology for supporting analysis is demanded.
As a technology for supporting the analysis, databases in which the gene interaction networks are systematically accumulated while classifying the gene interactions according to types of diseases or in vivo molecular mechanism are known (for example, Kyoto Encyclopedia of Genes and Genomes (KEGG), [online], searched on Feb. 27, 2006, and BioCarta, [online], searched on Feb. 27, 2006. The interactions related the gene clusters of interest can be analyzed depending on disease or in vivo molecular mechanism, using of the databases.
Japanese Patent Laid-open Publication No. 2003-44481 discloses a technology for sorting genes that have high possibility to be related to a disease (disease-related gene). The sorting is performed in a gene interaction network as shown in FIG. 21.
According to the technology, identification of “research genes” is possible. To a gene cluster in which the differences in the gene expression states is observed when a certain drug is administered and when the drug is not administered, a gene clustering result, based on an appearance of a gene in a document, is presented. A relationship between the document in which the gene appears and the drug and the disease of interest is also presented. As a result, a gene that reacts to the drug and is expected to have relation with the disease of interest but has not been reported, is identified as the “research gene”.
When a database, such as KEGG and BioCarta, is used, coverage of the database may be insufficient. The database is created manually, based on research findings acquired in a field of molecular biology. Therefore, gene interactions that are still being researched are not included. As a result, it cannot be expected that all gene interactions included in the gene interaction network 2100 are included in the database.
Furthermore, in the conventional technology disclosed in Japanese Patent Laid-open Publication No. 2003-44481, humans are required to interpret the gene interaction network 2100, while focusing on a specific drug and a specific disease, and judge relativity between individual genes and the disease. Therefore, it is difficult to apply the conventional technology as a support technology when there are plural diseases of interest.
In other words, presented information is required to be interpreted manually for each possible disease. Therefore, an efficient discovery and sorting of a disease-related gene is difficult. In general, one factor can increase a risk of plural diseases. Therefore, a technology for supporting analysis considering multiple diseases is demanded.